In last few years, as a new supervised learning paradigm, label distribution learning (LDL) has been applied to many fields and shown good results in these fields, such as face age estimation, head posture estimation, movie score prediction and crowd count in public video surveillance. Recently, the correlations between labels have been considered in some algorithms when solving the problem of label distribution learning. However, most of the existing methods take label correlations as a prior knowledge, which may not be able to correctly describe the real relationship between labels. In addition, label correlations are usually used to regularize the hypothesis space in the training phase, while the final label distribution prediction does not use these correlations explicitly. Therefore, this study proposes a new label distribution learning method, label distribution learning with collaboration among labels (LDLCL), which aims to explicitly consider the correlated predictions of labels while training the expected model. Specifically, the hypothesis is first proposed: for each label, the final prediction involves the cooperation between its own prediction and other labels' predictions. Based on this assumption, a new method is proposed to learn label correlations by sparse reconstruction in label space. Then, the learned label correlations are seamlessly integrated into model training, and finally the learned label correlations are used in label prediction. Sufficient experimental results show that the proposedapproach is superior to other similar methods.